An improved bearing fault detection strategy based on artificial bee colony algorithm

The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.

Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network System

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

The Objective Function Value Optimization of Cloud Computing Resources Security Allocation of Artificial Firefly Algorithm

Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.

Proton exchange membrane fuel cells modeling based on artificial neural networks

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

Approach to WTA in air combat using IAFSA-IHS algorithm

In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.

An improved artificial bee colony-random forest(IABC-RF)model for predicting the tunnel deformation due to an adjacent foundation pit excavation

An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be maintained.The improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation prediction.Results are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the site.Via the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.

基于改进DBSCAN空间聚类算法的北京市人工智能产业集聚格局研究

企业作为产业的重要主体,其发展直接表征着产业的发展,企业的空间格局对产业的发展及资源配置具有重要的引导意义。本文基于北京市工商注册在业的人工智能企业数据,提取企业注册地址并转化为地理位置信息,通过改进有噪声的应用背景下的基于密度的空间聚类算法(DBSCAN),分析北京市细粒度层级下人工智能产业集聚在全市域的空间分布格局特征。在DBSCAN算法改进方面,首先调整Minpts参数为企业注册资本总额与企业数量2个维度,企业数量大于5家且注册资本总额大于一定数额,为形成产业集聚区的2个必要条件;其次提取簇内位于边界的企业位置点作为集聚区地理边界点,将边界点连线并绘制形成人工智能产业集聚区。本文重点分析了企业注册资本和地理聚合半径对人工智能产业集聚区形成的影响,同时采用核密度估计法作为参照验证,表明改进DBSCAN方法具有精确刻画产业集聚区地理边界和确定不同规模产业集聚区的优势。通过分析得知,北京市人工智能产业集聚具有明显的中心分布特征,集中在城六区,呈现“两大龙头带动,北京市全域遍地开花”的分布情况,海淀区、朝阳区处于人工智能集聚程度高水平,相较其他区域,人工智能产业发展遥遥领先;西城区、东城区、丰台区、昌平区处于集聚程度较高水平;通州区、大兴区、平谷区、密云区、石景山区、房山区、门头沟区、怀柔区、顺义区处于集聚程度中等水平;延庆区集聚程度较低。通过改进DBSCAN算法精确定位出中关村区域、上地西二旗区域、五道口区域、望京区域、国贸区域、亦庄经开区等人工智能产业集聚区。进一步探究发现,海淀区的人工智能科研人才优势,朝阳区的信息技术领域企业基础,是两区人工智能发展突出的直接原因。延庆区等郊区远离北京市中心城区,产业资源匮乏,同时由于区域功能定位限制等原因,导致人工智能产业集聚水平较低,表明这些区域人工智能产业发展较为缓慢。

数控压机伺服控制系统复合控制器I-ABC与PID优化

为了提高数控压机伺服控制系统的控制精度,针对伺服控制系统运行控制过程构建数学模型,在人工蜂群算法基础上融入了云分析模型,之后采用改进人工蜂群算法(Improved Artificial Bee Colony,I-ABC)调节比例-积分-微分(Proportional Integral Differential,PID)参数,建立了一种复合控制方法。研究结果表明:以I-ABC进行PID控制时,可以使幅度差降低到0.4%,相位差基本在-0.54°之内,系统加载精度也获得了明显提升,表现出了更优的跟踪性能。以I-ABC进行PID控制时,能够对多余力起到明显抑制作用,响应速度也获得明显提升,可以有效满足系统的准确控制要求。在系统内加入干扰信号,引入I-ABC实施PID调节可以减小系统超调量,还可以获得更短调节时间,使系统获得更强抗干扰性能。

改进RRT^(*)-APF-DP融合算法的机械臂路径规划

针对基本的快速拓展随机树算法(rapidly-exploring random tree,RRT^(*))存在搜索随机性大、效率低、路径非最优的缺点,提出一种引入人工势场法算法(artificial potential field method,APF)和Douglas-Peucker算法的改进RRT^(*)-APF-DP路径规划算法.在RRT*算法的采样点生成阶段引入变采样范围偏置搜索与步长自适应调整策略,融合重新设计的APF算法的引力与斥力函数,增强路径扩展导向性与绕过障碍物能力.采用重采样策略改进DP算法,优化避障代价与控制点数量.实验结果表明,本算法规划的避障路径满足机械臂的运动要求,且算法规划的避障路径代价、规划时间和路径控制节点数均得到有效改善.

油田注水系统仿真模型参数修正方法研究

油田注水管网经过多年运行后,管道内壁由于腐蚀、结垢等原因致使其摩阻系数与原始数据不一致,导致管网平差计算和系统仿真模拟结果超出工程允许范围。由于经济原因和施工难度,不可能对整个管网节点进行压力和流量测试从而反演求解出各管段真实的摩阻系数。为此,以管网各管段摩阻系数为研究对象,以管网有限个压力测试点为已知参量,建立管网摩阻系数反演模型和测压点分布模型,利用改进的人工蜂群算法对反演模型进行最优化求解,计算结果在工程误差允许范围。

基于AIS轨迹和改进蚁群算法的船舶航线规划方法

在保证船舶航线安全的前提下,以最短航程为目标,提出基于AIS轨迹和改进蚁群算法的船舶航线规划方法。对船舶AIS数据进行预处理,去除船舶AIS数据中的冗余数据,完成船舶AIS数据提纯;采用基于粒子群与K均值混合聚类算法的核心转向点筛选与识别方法,筛选并识别船舶AIS数据中船舶航线核心转向点数据;通过基于改进蚁群算法的航线规划方法,以核心转向点数据为基础,构建航线网络,在此网络中,通过人工势场法对蚁群算法进行改进,对船舶航线进行寻优,实现船舶航线规划。经实验验证,本文方法能够规划出安全合理的船舶航线。

一种改进人工势场的矿车避障路径规划方法

为克服传统人工势场算法以局部最优解为目标造成路径规划中路径偏离过于频繁的问题,提出了基于改进人工势场算法的动态避障路径规划方法。该方法通过格栅图法无人驾驶矿车行进路线和环境进行建模,进而形成连接了起始点和目标点的网格图。针对障碍规避问题,根据矿区路径特点建立引力势场、斥力势场以及综合势场模型,模型中改进了人工势场函数,并对势场作用区域进行优化,构建强化远距点斥力为主的综合势场模型。考虑到矿山的山地场景环境比较单一,改进算法通过仿真实验模拟道路的方式对搜索路径进行限制,使无人驾驶矿车在既定限制空间内搜索最佳路径。仿真结果表明,路径规划算法能较大程度避免轨迹偏离以及局部震荡问题,改善了矿车行进效率。

移动机器人避障路径多激光传感器融合规划技术

为控制移动机器人成功躲避静、动障碍物,安全快速运行至目的地,研究移动机器人避障路径多激光传感器融合规划技术。此技术在机器人身上安装2个激光雷达传感器,感知机器人所处环境障碍信息,由基于多激光传感器融合的障碍物定位方法,将2个激光雷达传感器感知信息加权融合,提取静态障碍物位置数据后,使用基于动态避障的机器人路径规划方法,改进A∗算法结合静态障碍物位置数据,规划机器人避障的全局避障路径;考虑到全局避障路径中会有可能出现动态障碍物,由人工势场法,计算动态障碍物与目的地对机器人的斥力、引力、合力以及合力方向,调节机器人在全局避障路径中,动态避障的运行方向。实验中,本方法下对障碍物定位偏差在0.1 m内,此技术对静、动障碍物的避障能力合格,运行至目的地时,与障碍物0次碰撞,规划路径和运行时间较短,分别为158 m和9.2 min,效率较高。

基于功角稳定性的区域电网储能选址定容方法

为了缓解跨传输断面的调峰压力,同时提升电网的功角稳定性,建立了兼顾经济性与功角稳定性的储能选址定容双层优化模型,并给出了模型的求解方法。上层模型考虑区域电网的运行经济性,以储能电站收益最大为优化目标,确定储能配置总容量。下层模型考虑区域电网的功角稳定性,以储能调度期间总网损最小和储能调节区域电网功角稳定性能力最大为优化目标,进行储能选址与总容量的分配。双层模型的上层采用遗传算法进行求解,下层采用改进多目标人工蜂群算法进行求解。以某实际电网为算例进行仿真验证,结果表明,根据所提双层优化模型进行储能配置可以在保证经济性的同时,提升区域电网的功角稳定性。

静电荷逃逸电子式电压互感器的电场优化方法

针对静电荷逃逸电子式电压互感器(electronic voltage transducer,EVT)电场边缘效应引入的附加电容影响其测量准确性的问题,提出一种粒子群优化算法(particle swarm optimization,PSO)、风驱动优化算法(wind driven optimization,WDO)、最小二乘支持向量机(least squares support vector machine,LSSVM)结合的电场优化方法.首先,采用WDO-LSSVM构建描述EVT电场分布的数学模型,其中用WDO优化LSSVM的参数选择.其次,采用PSO算法求解上述数学模型,获得EVT电场分布最优时的物理参数组合.同时,提出一种电极结构优化方法,将电极边缘切削成圆弧或斜面以改善电场局部集中现象.最后,采用COMSOL仿真软件构建EVT的三维模型,对EVT进行电流-电路耦合场仿真.仿真结果表明,所提方法减小了边缘效应引入的附加电容和EVT的测量误差.

Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony （ABC） algorithm optimized error minimized extreme learning machine （EM-ELM） is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved.

基于碰撞检测的双机械臂避障路径规划

针对协同作业的双机械臂系统,提出一种不依赖于外部传感器和无需动力学建模的实时避障路径规划方法。基于运动学和混合包围盒法简化机械臂模型与障碍物,经投影相交法快速判断干涉的可能性,将碰撞检测转化为求解最短距离,再结合速度场以及添加虚拟约束点和考虑障碍物边界来改进传统人工势场法。最后,通过MATLAB-CoppeliaSim联合仿真验证包含静态和动态障碍物的碰撞检测算法。结果表明:该碰撞检测算法适应动、静两种复杂障碍物环境,通用性较高,生成的避障路径能够跳出极小值和实现目标可达,有效保障人机交互及设备的安全性。

基于改进UCT算法的爱恩斯坦棋博弈设计研究

为了解决传统UCT算法在爱恩斯坦棋中评估精度低和搜索效率低的问题,文中以基于UCT算法的爱恩斯坦棋为研究对象,通过基于修正值的收益值计算方法和并行优化处理,对传统UCT算法的估值方法和线程问题进行了改进,实现了在有限时间内达到更优的搜索精度和搜索效率。结果表明,改进的多线程UCT算法在修正系数为2.5时的收益值以及胜率明显大于传统单线程UCT算法,且在搜索效率上具有显著提高。

基于自适应VMD的光储微电网容量优化配置方法

光储微电网容量配置寻优过程较为复杂,导致光储微电网经济效益下降。为有效解决这一问题,设计基于自适应VMD的光储微电网容量优化配置方法。将自适应VMD与改进阈值函数相结合实施光储微电网电能质量扰动检测。根据电能质量检测结果构建微电网容量优化配置目标函数。基于人工鱼群算法与粒子群算法设计多目标AFS-PSO算法,实施目标函数的寻优,得到光储微电网容量配置的最优解,实现微电网容量优化配置。测试结果表明,在不同超级电容储能占比下,该文设计方法的年经济效益均保持在较高的水平,实际应用效果好。